This thesis describes the procedure employed in writing and testing programed instruction on the subject of biosystematics. It briefly discusses similar studies that have been done with programed and other self-instructional materials. A review of the literature provides evidence that there is a need for such instructional materials in our modern schools.The thesis then describes the procedure that the writers followed in the writing, testing, and revising of the programed text, Principles of Biosystematics. Recorded in the appendices are the testing data which include the students# pre- and posttest scores and the item-analysis of the examinations used to test the first and second drafts of the program. These data were used to determine what parts of the program seemed weak or poorly developed, Such segments of the program were modified before being incorporated in the final draft of the programed textbook which, along with the accompanying teacher's manual, is also located in the appendices.

Controlling spatial-temporal gene expression patterns is a fundamental task for maize growth and development. With the emergence of massively parallel sequencing, genome-wide expression data production has reached an unprecedented level. This abundance of data has greatly facilitated maize research, but may not be amenable to traditional analysis techniques that were optimized for other data types. In one project, using publicly available data, a Gene Co-expression Network (GCN) was constructed and used for gene function prediction, candidate gene selection and improving understanding of regulatory pathways. To build an optimal GCN from plant materials RNA-Seq data, parameters for expression data normalization and network inference were evaluated. A comprehensive evaluation of these two parameters and ranked aggregation strategy on network performance using libraries from 1266 maize samples was conducted. Three normalization methods (VST, CPM, RPKM) and ten inference methods, including six correlation and four mutual information (MI) methods, were tested. The three normalization methods had very similar performance. For network inference, correlation methods performed better than MI methods at some genes. Increasing sample size also had a positive effect on GCN. Aggregating single networks together resulted in improved performance compared to single networks. In another project, a maize mutant, transgene reactivated 9-1 (tgr9-1) in the transcriptional gene silencing (TGS) pathway, was cloned. The B-A translocation lines were used to map tgr9-1 on chromosome 3 and this result was consistent with molecular markers. To further locate tgr9-1, next-generation sequencing (NGS) combined with bulk segregant analysis was applied to the tgr9-1 mapping population. Using coexpression analysis, our result indicates a maize dicer-like3a (Zmdcl3a) gene is a high-confidence candidate gene for tgr9. Zmdcl3a is involved in the RNA-directed DNA methylation (RdDM) pathway. This pathway is driven by two plant-specific DNA-dependent RNA polymerases, Polymerase IV (Pol IV) and Polymerase V (Pol V). Several kinds of non-coding RNAs are involved, including long single-stranded RNAs, double-stranded RNAs, and small interfering RNAs. The identification of tgr9-1 uncovered the role of non-coding RNAs in TGS and revealed the diversity of TGS pathways in maize. One primary focus of gene regulation study is by studying transcription factors (TFs). Transcription factors (TFs) are proteins that can bind to DNA sequences and regulate gene expression. Many TFs are master regulators in cells that contribute to tissue-specific and cell-type-specific gene expression patterns in eukaryotes. Little is known about tissue-specific gene regulation through TFs in maize. In this project, a network approach was applied to elucidate gene regulatory networks (GRNs) in four tissues (leaf, root, shoot apical meristem and seed) in maize. We used GENIE3 machine-learning algorithm combined with the large quantity of RNA-Seq expression data to construct four tissue-specific GRNs. Although many TFs were expressed across multiple tissues, a multi-tiered analysis predicted tissue-specific regulatory functions for many transcription factors. Some well-studied TFs emerged within the four tissue-specific GRNs, and the GRN predictions matched expectations based upon published results for many of these examples. The GRNs were also validated by ChIP-Seq datasets (KN1, FEA4, and O2). Key TFs were identified for each tissue and matched expectations for key regulators in each tissue, including GO enrichment and identity with known regulatory factors for that tissue. / A Dissertation submitted to the Department of Biological Science in partial fulfillment of the requirements for the degree of Doctor of Philosophy. / Spring Semester 2018. / April 2, 2018. / GENE EXPRESSION, MAIZE, NETWORK, RDDM, SMALL RNA, TRANSCRIPTION FACTOR / Includes bibliographical references. / Karen M. McGinnis, Professor Directing Dissertation; Alan R. Lemmon, University Representative; Kathryn M. Jones, Committee Member; Brian P. Chadwick, Committee Member; Jonathan H. Dennis, Committee Member.

Genome-wide RNAi screening has emerged as a powerful tool for loss-of-function studies that may lead to therapeutic target discovery for human malignancies in the era of personalized medicine. However, due to high false-positive and false-negative rates arising from noise of high-throughput measurements and off-target effects, powerful computational tools and additional knowledge are much needed to analyze and complement it. Availability of high-throughput genomic data including gene expression profiles, copy number variations from large-sampled primary patients and cell lines allows us to tackle underlying drivers causally associated with tumorigenesis or drug-resistance. In my dissertation, I have developed a framework to integrate functional RNAi screens with systems biology of cancer genomics to tailor potential therapeutics for reversal of drug-resistance or treatment of aggressive tumors. I developed a series of algorithms and tools to deconvolute, QC and post-analyze high-throughput shRNA screening data by next-generation sequencing technology (shSeq), particularly a novel Bayesian hierarchical modeling approach to integrate multiple shRNAs targeting the same gene, which outperforms existing methods. In parallel, I developed a systems biology algorithm, NetBID2, to infer disease drivers from high-throughput genomic data by reverse-engineering network and Bayesian inference, which is able to detect hidden drivers that traditional methods fail to find. Integrating NetBID2 with functional RNAi screens, I have identified known and novel driver-type therapeutic targets in various disease contexts. For example, I discovered that AKT1 is a driver for glucocorticoid (GC) resistance, a problem in the treatment of T-ALL. The inhibition of AKT1 was validated to reverse GC-resistance. Additionally, upon silencing predicted master regulators of GC resistance with shRNA screens, 13 out of 16 were validated to significantly overcome resistance. In breast cancer, I discovered that STAT3 is required for transformation of HER2+ breast cancer, an aggressive breast tumor subtype. The suppression of STAT3 was confirmed in vitro and in vivo to be an effective therapy for HER2+ breast cancer. Moreover, my analysis revealed that STAT3 silencing only works in ER- cases. Using my framework, I have also identified potential therapeutic targets for ABC or GCB-type DLBCL and subtype-based breast cancer that are currently being validated.

Cancer research, like many areas of science, is adapting to a new era characterized by increasing quantity, quality, and diversity of observational data. An example of the advances, and the resulting challenges, is represented by The Cancer Genome Atlas, an enormous public effort that has provided genomic profiles of hundreds of tumors of each of the most common solid cancer types. Alongside this resource is a host of other data and knowledge, including gene interaction databases, Mendelian disease causal variants, and electronic health records spanning many millions of patients. Thus, a current challenge is how best to integrate these data to discover mechanisms of oncogenesis and cancer progression. Ultimately, this could enable genomics-based prediction of an individual patient's outcome and targeted therapies, a goal termed precision medicine. In this thesis, I develop novel approaches that examine patterns in populations of cancer patients to identify key genetic changes and suggest likely roles of these driver genes in the diseases. In the first section I show how genomics can lead to the identification of driver alterations in melanoma. The most recurrent genetic mutations are often in important cancer driver genes: in a newly sequenced melanoma cohort, recurrent inactivating mutations point to an exciting new melanoma candidate tumor suppressor, FBXW7, with therapeutic implications. But each tumor is unique, underlining the fact that recurrence will never capture all relevant mutations responsible for the disease. Tumors are a result of random events that must collaborate to endow a cell with all of the invasive and immortal properties of a cancer. Some combinations of events are lethal to a developing tumor, while other combinations are simply not preferentially selected. In order to discover these complex patterns, I develop a method based on the joint entropy of a set of genes, called GAMToC. Using GAMToC, I identify sets of recurrently altered genes with a strongly non-random joint pattern of co-occurrence and mutual exclusivity. Then, I extend this method as a means of identifying novel genes with a role in cancer, by virtue of their non-random pattern of alteration. Insights into the roles of these novel drivers can come from their most strongly co-selected partners. In the final section of the main text, I develop the use of cancer comorbidity, or increased cancer risk, as a novel data source for understanding cancer. The recent availability of clinical records spanning a large percentage of the American population has enabled discovery of many cancer comorbidities. Although most cancers arise as a result of somatic mutations accumulating over a patient's lifespan, mutations present at birth could predispose some rare populations to increased cancer risk. Mendelian disease phenotype provides strong insight into the genotype of an afflicted individual. Thus, if Mendelian diseases with cancer comorbidity can be shown to have specific defects in processes that are important in the development of that cancer, statistical comorbidity could provide a new a resource for prioritizing Mendelian disease genes as novel cancer related genes. For this purpose, I integrate clinical comorbidity, Mendelian disease causal variants, and somatic genomic profiles of thousands of cancers. I demonstrate that comorbidity indeed is associated with significant genetic similarity between Mendelian diseases and the cancers these patients are predisposed to, suggesting highly interesting and plausible new candidate cancer genes. While cancer may be the result of a series of selected random events, patterns of incidence across large populations, as measured by genomics or by other phenotypes, contain much non-random signal yet to be mined.

This study examines the evolutionary history of the cosmopolitan beaksedge tribe Rhynchosporeae (ca. 386 spp.; Cyperaceae) using phylogenetics. Taxon sampling covers 25 of the 28 taxonomic sections proposed for the tribe. I compare a history inferred for Rhynchosporeae using a single plastid gene (Chapter 2) with one inferred using hundreds of loci (Chapter 4). The latter involves a sequencing methodology I develop with collaborators that can be applied across angiosperms (Chapter 3). Chapter 2 recognizes that Rhynchosporeae has high levels of endemicity (≥ 44%) in tropical and subtropical American savannas and can provide insights into the diversification of their biotas. Wind pollination, occupation of a savanna habitat, and a C3 photosynthetic pathway are common in the tribe, but showy (presumably insect-pollinated) inflorescences, occupation of forest habitat, and a C4 pathway also occur. I reconstructed a dated phylogenetic hypothesis for 79 taxa, using the trnL/F plastid region, inferring a mean crown-group age of 56 million years. Fitch parsimony infers the most recent common ancestor (MRCA) to have occupied a savanna habitat with eight or more shifts to forest. Features associated with insect pollination—white bracts and spikelets—were shown to evolve six or more times but were not correlated with the shifts to forest habitat where wind pollination is likely to be less effective. I found evolutionary correlations in the pairwise comparisons of bract color versus spikelet color and bract positioning versus bract color. Members with anatomies associated with C4, though anatomically variable, form a clade with a crown age of 19 million years. In Chapter 3, with collaborators I develop a robust probe design process to identify 499 low-copy nuclear regions and 18 high-copy functional genes for hybrid enrichment. We obtained >90% enrichment success for target regions. Between 159 and 488 orthologs were retained in alignments used for phylogenetic inference at deep and shallow levels across the angiosperms. A sampling strategy focusing on incremental removal of incongruent loci in combination with removal of sites with high rates of change produced 196 alignments for phylogenetic inference. The phylogenetic hypotheses at each sample level represent outcomes under different regions of parameter space. These outcomes were presented using heatmaps that depict bootstrap support at all nodes for those 196 levels of parameter space. This provided a new approach for sensitivity analyses and for testing the robustness of any hypotheses. A randomization methodology for hypotheses testing at specific nodes takes advantage of the heatmap approach. Focusing on the difficult-to-resolve eudicot, monocot, Magnoliid nodes the analysis revealed that the supermatrix approach produced a spuriously confident yet conflicting result in some regions of parameter space. With >97% of the data, supermatrix analyses supported eudicot and Magnoliids as sister. Support switched to strongly support the eudicot and monocot sister relationship at higher levels of data removal. In contrast the coalescent model consistently supported the latter relationship across most of the parameter space. Overall the eudicot and monocot sister relationship is robustly favored. In Chapter 4, I reexamine beaksedge (tribe Rhynchosporeae ) diversification but employ the anchored hybrid enrichment protocols developed in Chapter 3. A dated phylogenetic hypothesis for 115 taxa in the tribe and 11 outgroup taxa inferred a mean crown-group age for the tribe of 43.2 million years. Ancestral state reconstruction using stochastic mapping infers an open (savanna) habitat for the MRCA. This was the common state along 77% of the total branch lengths. However, there was an average of 22 independent shifts from open habitats into forest understory or edges in its descendants. The common state was the typical seasonally wet savanna soils. The state associated with their occurrence in dry, well drained soils was reconstructed for 4% of the total branch lengths, but there was an average of 11.2 transitions to that state. There were 3.7 transitions to the state where plants typically occur in standing or flowing water. An average of 5.9 transitions from nondescript brown or green inflorescences associated with wind pollination to those associated with insect pollination (white spikelets and/or bracts) were inferred but these were not correlated with the shifts to forest habitat. Members with C4 anatomy formed a clade that diverged from a sister clade containing taxa with C3 photosynthetic anatomies 26 MYA; this is earlier than previously thought. Most of the taxonomic sections described by Shirley Gale and Georg Kükenthal for Rhynchospora and Pleurostachys were not monophyletic. I also briefly discuss the possible significance of detecting a recently described repetitive satellite DNA element known to be associated with the centromeric protein CENH3. / A Dissertation submitted to the Department of Biological Science in partial fulfillment of the Doctor of Philosophy. / Fall Semester 2016. / November 14, 2016. / Anchored Phylogenomics, Angiosperms, evolution, photosynthesis, pollination, Rhynchospora / Includes bibliographical references. / Austin Mast, Professor Directing Dissertation; William Parker, University Representative; Alice Winn, Committee Member; Scott Steppan, Committee Member; Brian Inouye, Committee Member.

High throughput sequencing data are rich in information and contain many off-target sequences (reads) that are often ignored but may be biologically relevant. Seed extension, a combination of reference and de novo based assembly methods, can be used to extract the information but it is time-consuming to implement because it requires that multiple seeds (sequences from one or many closely related species) be gathered in advance. A new tool is presented here, SeedSQrrL, that can automatically crawl the web to gather the seeds from the closest taxonomic relative for each gene and store it into a relational database. The seeds can then be used to create multiple seed extensions which are later combined into a reference or used for downstream phylogenetic analysis. Patterns in the resulting gene trees can be searched for using the traditional methods of tree comparison (Robinson-Foulds topological distance and branch-length comparison methods). Currently, no open source tree pattern matching program exists that allows the user to modify algorithms and create their own custom pattern matching functions. I have worked on such a tool, called Treematcher, and it will be made available in the ETE Toolkit (a Python Environment for Tree Exploration). Three biological case studies will be included included to demonstrate the capabilities of the two programs: 1) a custom function in Treematcher to perform a regular expression-like query, 2) SeedSQrrL will be used to isolate mitochondrial genes from snakes and chloroplast genes from angiosperms, and 3) a large case study of animals will be assembled. / A Dissertation submitted to the Department of Scientific Computing in partial fulfillment of the requirements for the degree of Doctor of Philosophy. / Spring Semester 2018. / April 2, 2018. / Automated Gene Reference Collection, Gene Tree Pattern Matching, High Throughput Sequence Analysis, NCBI Taxonomy, Open Source Software for Bioinformatics, Python / Includes bibliographical references. / Alan Lemmon, Professor Directing Dissertation; Michelle Arbeitman, University Representative; Anke Meyer-Baese, Committee Member; Peter Beerli, Committee Member; Dennis Slice, Committee Member.

The Andes, along with the Amazon and Atlantic forests, harbor the richest avifauna in the world with roughly one third of all the world's species of birds. Many biogeographical studies have sought to explain the origin and diversification of Andean taxa. However, because of the Andes' extensive latitudinal span and complexity, there is no one single cause of origin or of diversification that can explain the diversity found in them. Along the Andes, multiple biogeographic patterns of disjunction between highland and lowland sister-groups have been linked to Andean uplift. For example, Ribas et al. (2007) provided evidence that the spatio-temporal diversification in the monophyletic parrot genus Pionus is causally linked to Andean tectonic and palaeoclimate change through vicariance. Thus, if the Andes uplift is responsible for some of the patterns of montane-lowland disjunctions, it may be one of the mechanisms underlying the taxonomic assembly of the Andean montane avifauna. In this dissertation I explored whether the origin and diversification of three groups of Andean birds--the exclusively Andean parrot genera Hapalopsittaca, the subclade of mangoes containing Doryfera, Schistes, and Colibri, and the ovenbirds of the tribe Thripophagini--can be linked to Earth history. The results show that the origin of these Andean taxa can be explained through vicariance from their lowland sister-groups, mediated by the uplift of the Andes. Thus, this thesis proposes that geological events are directly responsible for originating diversity throughout montane environments. Once in the Andes, the diversification of these montane taxa can be explained by events such as the tectonic evolution of the Andes--which created canyons and valleys that may have caused the vicariance of continuous populations--as well as by the climatic oscillation of the Pleistocene, which caused altitudinal shifts, expansion, and contraction of the montane vegetation belts during the climatic oscillations of the Pleistocene.

Having outlined the present situation as regards rival taxonomic philosophies, and some of its historical background, the thesis examines this attempt to recategorize taxa as individual-like entities, and finds it wanting. The properties of species which render them regardable as individuals do not readily extend to more inclusive levels, or, if they do, are not readily restricted solely to cladistic taxa. Cladistic systematization, in moving away from the notion of a taxon as a class of similar entities, may cease to convey the information expected of a classification system. The practice of biology requires a more flexible and more stable taxonomy than can be provided by strict adherence to cladistic rules, and taxa are-better regarded as 'historical classes', delineated neither by pure unanalysed similarity nor by logical transformation of hypotheses of phylogenetic relationship, but by a considered pragmatic synthesis of the two, employing the notion of convexity as a criterion of acceptability.

The mainly tropical genus Lygodium differs from other ferns in that the fronds are indeterminate and are vine-like. A single species, L. palmatum is native in temperate North America. The temperate Asian L. japonicum is naturalized throughout much of the southeastern United States. About twenty years ago, L. microphyllum was introduced into South Florida and is now naturalized in several counties. The present study documents differences among spores and their generation, development of sporophytes from the fertilized egg, and in flavonoid chemistry. Hybridization experiments showed a strong possibility for cross fertility between species. Experiments with prothallial development and differentiation revealed that environment influenced variation and gametangium formation. Greater similarity in sporophyte developmental stages and in frond phytochemistry show that the native L. palmatum is phenetically closer to the tropical L. microphyllum than to L. japonicum. All three species are clearly distinct at all levels examined.

There is growing evidence that RNAs compete for binding and regulation by a finite pool of microRNAs (miRs), thus regulating each other through a competing endogenous RNA (ceRNA) mechanism. My dissertation work focused on systematically studying ceRNA interactions in cancer by reverse-engineering context-specific miR-RNA interactions and ceRNA regulatory interactions across multiple tumor types and study the effects of these interactions in cancer. I attempted to use ceRNA interactions to explain how genetic and epigenetic alterations are propagated to target established drivers of tumorigenesis. Using bioinformatics analysis of primary tumor samples and experimental validation in cell lines, I have investigated the roles that mRNAs and noncoding RNAs can play in tumorigenesis via ceRNA interactions. Specifically, I studied how RNAs target tumor-suppressors and oncogenes as ceRNAs, and attempted to accounting for some of the missing genomic variability in tumors.